1. Field of the Invention
The invention described herein is related to image processing of biometric data to compensate for image skew. More particularly, the invention is related to methods and associated systems for transforming biometric data so that features may be extracted consistently therefrom regardless of the original orientation of the biometric data in the image.
2. Description of the Prior Art
In the past decade, vast resources have been put into motion towards improving systems and methods for authenticating persons by automatic means. Machine authentication of persons spans several fields of endeavor, including network security, financial transaction authorization and the electronic execution of binding agreements and legal documents.
The field of biometrics, which utilizes physiological or behavioral phenomena that are particularly unique to an individual, has introduced many systems and methods for authentication, many of which are enjoying some popularity. Biometric data includes fingerprint, iris and face images, and voice and handwriting samples. But, while biometric systems and methods are becoming more prevalent, a greater reliance on the technology has been hampered due to the complex nature of the data metric itself.
One particular problem is that of the inconsistencies in biometric data. Indeed, extracting features from a biometric sample comes with its own complications in implementation, but when variability in the way an individual submits a biometric sample is introduced, consistent extraction of features becomes much more difficult. For example, in handwriting analysis, it is often the case that a signer will enter a sample at a different angle with respect to a previous sample upon which a feature template has been processed and stored for validation purposes. Whereas, to the trained human eye, it is apparent that a slanted version of a handwriting sample originates from the same person that signed a non-slanted version of the sample, automating the extraction of handwriting features to accomplish the recognition task by machine is complicated when the sample is skewed from the orientation on which a template was based.
Previous attempts to rectify images to a common orientation have primarily involved regression techniques to determine a regression line, which is then used as a reference for locating pertinent features for extraction. Examples of this type of technique are disclosed in U.S. Pat. No. 5,563,403 issued to Bessho, et al., U.S. Pat. No. 6,084,985 issued to Doffing, et al., and U.S. Pat. No. 5,892,824 issued to Beatson, et al, the latter patent having common inventorship with the present invention. These methods suffer from the dependency of the extraction process on the angle at which the data was submitted. Thus, key features extracted from the image for biometric verification are more variable and therefore less useful than if the image were rotated to a consistent and repeatable angle of inclination prior to feature extraction.
U.S. Pat. No. 5,828,772, issued to Kashi, et al., discloses normalization of signature data by certain ones of the signature's Fourier descriptors to reestablish the signature data in a common orientation. The normalization disclosed in Kashi, et al. translates the signature by the value of the zero-th, or “D.C.” descriptor and scales and rotates the signature according the first Fourier descriptor. However, the normalization described in Kashi, et al. is highly dependent on the sequential ordering of data points in the signature. For example, the rotation angle of the normalization depends on the location of the first point in the sequence with respect to its centroid. This sequential dependency severely limits the applicability of the normalization technique in that not all biometric data are arranged in a sequence. Many types of biometric data, such as that found in fingerprint and iris images, do not have among their properties a start and end point. The Fourier normalization disclosed in Kashi, et al. could not be applied to such non-serialized data. Moreover, because the normalization of Kashi, et al. requires Fourier analysis, it may be too costly to implement on certain platforms.
In light of the prior art, there is an apparent need for a system and associated methods for transforming biometric image data so that features can be consistently extracted therefrom regardless of the angle at which the data was submitted.